Doping in Cycling: Realism, Antirealism and Ethical Deliberation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Professional road cycling in general and the Tour de France in particular have a tarnished \nreputation as far as the illegal and illegitimate use of performance enhancing \ndrugs is concerned. Numerous positive dope tests each year are, for some, testament \nto the insidious corruptness of cyclists, their entourage, and the practice community. \nFor others, it attests to both the strength of the commitment to drug free sport and the \nrigor of the processes implemented to achieve it. In a recent interview on British TV, \nMark Cavendish a winner of 6 Tour de France stages in 2009, claimed that no other \nsport was as committed to clean competition as road cycling1. Although standard \nantidoping arguments have been presented, discussed, and widely rehearsed in the \nliterature, consensus on the matter has not been reached neither in the community of \nsports ethicists nor, as I will suggest, in the practice community of elite road cyclists. \nIn this paper I explore a possible defense of doping in elite cycling which requires us \nto think carefully about common assumptions about both the nature and purpose of \ndoping. In particular I examine the way in which both realists and antirealists might \ndeal with a particular prodoping argument.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it